Mobile-4DGS：统一的静态-动态实时移动高斯溅射
Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting
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中文总结 AI 辅助
Mobile-4DGS提出统一轻量级框架，通过能量聚合、属性增强和深度顺序重用，实现移动端实时高保真静态与动态高斯溅射，大幅降低存储和渲染开销。
中文摘要 AI 辅助
三维高斯溅射（3DGS）的最新进展在新视图合成中取得了显著性能，然而，由于存储开销大、基元冗余以及每帧计算成本高昂，在资源受限的移动设备上部署静态和动态高斯表示仍然具有挑战性。我们提出了Mobile-4DGS，一个用于在移动平台上进行高保真实时静态和动态高斯渲染的统一轻量级框架。为了实现紧凑的外观建模，我们引入了一种蒙特卡洛镜面能量聚合器，将高阶辐射残差压缩为一阶球谐函数（SH），并配合一个属性条件SH增强模块，其预测的偏移量在推理前预先烘焙。我们进一步提出了一种基于多视图Alpha的致密化和剪枝策略，以抑制冗余基元同时保持多视图一致性。对于动态场景，我们通过构建二阶高斯运动、可学习的时域支撑以及二元静态-动态划分，开发了一种紧凑的显式4D表示，实现了无需运行时变形网络的连续时间建模。基于该划分，一个深度顺序证书选择性地重用先前提交的深度顺序，以减少播放期间的重新投影、排序、合并和索引缓冲区更新。在静态和动态场景上的大量实验表明，Mobile-4DGS大幅减少了存储和渲染开销，同时保持了有竞争力的视觉质量，实现了移动设备上的实时3D和4D高斯溅射。代码已发布：此https URL。
英文摘要
Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaussian representations on resource-constrained mobile devices remains challenging due to heavy storage, redundant primitives, and costly per-frame computation. We present Mobile-4DGS, a unified lightweight framework for high-fidelity real-time static and dynamic Gaussian rendering on mobile platforms. For compact appearance modeling, we introduce a Monte Carlo Specular Energy Aggregator that compresses high-order radiance residuals into the first-order Spherical Harmonics (SH), together with an Attribute-Conditioned SH Enhancement module whose predicted offsets are pre-baked before inference. We further propose a Multi-View Alpha-Based Densification and Pruning strategy to suppress redundant primitives while maintaining multi-view consistency. For dynamic scenes, we develop a compact explicit 4D representation by constructing second-order Gaussian motion, learnable temporal support, and a binary static-dynamic partition, enabling continuous-time modeling without runtime deformation networks. Based on this partition, a Depth-Order Certificate selectively reuses previously committed depth orders to reduce re-projection, sorting, merging, and index-buffer updates during playback. Extensive experiments on static and dynamic scenes demonstrate that Mobile-4DGS substantially reduces storage and rendering overhead while maintaining competitive visual quality, enabling real-time 3D and 4D Gaussian Splatting on mobile devices. \textcolor{magenta}{\href{https://xiaobiaodu.github.io/mobile-4dgs-project/}{Code has been released: https://xiaobiaodu.github.io/mobile-4dgs-project/}}.
发表机构
- University of Technology Sydney（悉尼科技大学）
- Yale University（耶鲁大学）
- City University of Macau（澳门城市大学）
- Australian National University（澳大利亚国立大学）
- Adelaide University（阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。
